Ranging from vacation planning to survival tactics, few things have as much of an impact on our lives as the weather. Weather and climate matters factor crucially into many major business, economic, government, military, and individual decisions. Notwithstanding this critical role, weather conditions remain stubbornly difficult to predict with accuracy.
Many researchers in addressing climate prediction see the problem in terms of insufficient real time data regarding the huge and complex fluid dynamic systems that our atmosphere represents. Recent advances in computing technology now allow for the processing of vast amounts of data, and this provides for a better understanding of atmospheric conditions. However, there still exist significant weaknesses in the existing systems for capturing atmospheric data—critical data regarding atmospheric conditions that is otherwise critical for implementing the weather models that permit accurate predictions. In other words, we have very capable computer systems that process what data we have, but we need more and better data—indeed data collected very quickly or even in real time—to implement the processing that will give better predictions.
As an electromagnetic wave passes from space through a medium such as a planet's atmosphere, it is refracted and the phase and amplitude are modulated. The degree of this modulation is known as the refractivity of the atmosphere. If the refractivity for a portion of the atmosphere is determined, the atmospheric properties for that portion can be calculated or estimated. These relationships allow for determination of such atmospheric properties such as air pressure, temperature, water vapor pressure, ionosphere frequency and electron density temperature, pressure, air density, and water content.
Radio Occultation (RO) is a technique for determining atmospheric properties by observing how a radio wave behaves as it passes through an atmosphere. A radio receiver is positioned to receive the occulted radio wave and the degree of refraction and modulation are measured. From this, the refractivity is derived and atmospheric properties can be determined. Using this technique, a high degree of precision in the derived properties over a large vertical atmospheric section can be obtained. Furthermore, at longer radio wavelengths, cloud cover has little to no effect on the measurements, which provides tremendous advantage over existing atmospheric observation methods.
Existing weather satellite systems, such as NASA GOES, provide imaging data on cloud tops, but cannot routinely profile an entire atmospheric column. Using RO, atmospheric data along a relatively large vertical resolution can provide key insight into the three dimensional state of the atmosphere, which is crucial to climatology study.
A Global Navigation Satellite System (GNSS), such as GPS, provides excellent radio signal sources for RO study because they are a large network of existing satellites transmitting a reliable signal of a known quality at regular intervals. The precise location of the sources of GNSS signals relative to the Earth—an important part of RO measurements—are also known for any time of the day. Additionally, the large number of satellites provides many sounding opportunities for a single receiver satellite as it orbits, allowing the receiver satellite to take RO measurements across many “slices” of the atmosphere as it orbits and different occulted GNSS signals come within view.
A number of different GNSS systems are suitable for RO study. The United States's Global Positioning System (GPS) is well known and provides global coverage. Russia's Global Navigation Satellite System (GLONASS) also provides global coverage, but has been through periods of unreliability. The European Union's Galileo system is currently in development, but will provide global coverage with 35 satellites once operational. India's Regional Navigational Satellite System (IRNSS) and China's BeiDou Satellite Navigation System (BDS or Compass) are regional systems, but nevertheless provide suitable signals from which RO measurements can be taken.
There are existing missions to study GPS RO, most recently with the joint U.S./Taiwan FORMOSAT-3/COSMIC mission (COSMIC). Launched in 2006, the COSMIC project consists of six approximately 70 kg Low Earth Orbit (LEO) satellites each carrying GPS receivers and ionospheric photometer. GPS RO observations from COSMIC and previous missions have already improved weather predictions at many national forecast centers around the world. However, the COSMIC satellites are approaching the end of their operational lifetimes, while follow-on systems have encountered delays and funding issues, and are insufficient to meet the current and future demands of weather forecasting, climate monitoring and space weather prediction.
This highlights a major disadvantage of past and existing GPS RO systems. While relatively inexpensive when compared to traditional weather satellites, these systems still require institutional or governmental level funding for development, launch and maintenance. The custom, non-standard satellite platforms inevitably lead to a higher cost system that is also expensive to replace. Aging satellites rapidly degrade in the harsh environment of space. The technology aboard these satellites is also limited to the state of the art at the time that they were launched. The hardware of a satellite fleet cannot be upgraded and quickly becomes obsolete.
Higher cost satellite systems rely on fewer satellites. Having fewer receivers in orbit limits the number of global soundings, or RO observations, that are made within a given time period—a severe disadvantage when measuring a highly dynamic weather pattern, such as a hurricane. Fewer satellites also means lower geographic coverage as each satellite can only take RO measurements across a single slice of the atmosphere at one time.
Existing GPS RO satellites communicate directly with ground stations, which requires line of sight between the satellite and ground station. Non-geosynchronous orbiting satellites are within line of sight of a ground station for limited periods of time, which may result in delays in the delivery of a data package. This introduces further decreases in temporal resolution.